Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.8 KiB
Average record size in memory112.3 B

Variable types

Numeric12
Categorical2

Alerts

Student_ID is uniformly distributed Uniform
Student_ID has unique values Unique
Caffeine_Intake has 85 (17.0%) zeros Zeros

Reproduction

Analysis started2024-10-17 14:23:07.168796
Analysis finished2024-10-17 14:23:31.735578
Duration24.57 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Student_ID
Real number (ℝ)

Uniform  Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:31.892124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.48183
Coefficient of variation (CV)0.57677378
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2024-10-17T19:23:32.119555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 1
 
0.2%
1 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%
484 1
 
0.2%
483 1
 
0.2%
482 1
 
0.2%
481 1
 
0.2%
480 1
 
0.2%
479 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
500 1
0.2%
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%

Age
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.536
Minimum18
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:32.295082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q120
median21
Q324
95-th percentile25
Maximum25
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3331505
Coefficient of variation (CV)0.10833723
Kurtosis-1.2277948
Mean21.536
Median Absolute Deviation (MAD)2
Skewness-0.011547179
Sum10768
Variance5.4435912
MonotonicityNot monotonic
2024-10-17T19:23:32.463632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21 76
15.2%
25 72
14.4%
18 71
14.2%
20 64
12.8%
23 62
12.4%
24 58
11.6%
22 51
10.2%
19 46
9.2%
ValueCountFrequency (%)
18 71
14.2%
19 46
9.2%
20 64
12.8%
21 76
15.2%
22 51
10.2%
23 62
12.4%
24 58
11.6%
25 72
14.4%
ValueCountFrequency (%)
25 72
14.4%
24 58
11.6%
23 62
12.4%
22 51
10.2%
21 76
15.2%
20 64
12.8%
19 46
9.2%
18 71
14.2%

Gender
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Male
186 
Female
166 
Other
148 

Length

Max length6
Median length5
Mean length4.96
Min length4

Characters and Unicode

Total characters2480
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowMale
3rd rowMale
4th rowOther
5th rowMale

Common Values

ValueCountFrequency (%)
Male 186
37.2%
Female 166
33.2%
Other 148
29.6%

Length

2024-10-17T19:23:32.741850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T19:23:32.949295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 186
37.2%
female 166
33.2%
other 148
29.6%

Most occurring characters

ValueCountFrequency (%)
e 666
26.9%
a 352
14.2%
l 352
14.2%
M 186
 
7.5%
F 166
 
6.7%
m 166
 
6.7%
O 148
 
6.0%
t 148
 
6.0%
h 148
 
6.0%
r 148
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 666
26.9%
a 352
14.2%
l 352
14.2%
M 186
 
7.5%
F 166
 
6.7%
m 166
 
6.7%
O 148
 
6.0%
t 148
 
6.0%
h 148
 
6.0%
r 148
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 666
26.9%
a 352
14.2%
l 352
14.2%
M 186
 
7.5%
F 166
 
6.7%
m 166
 
6.7%
O 148
 
6.0%
t 148
 
6.0%
h 148
 
6.0%
r 148
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 666
26.9%
a 352
14.2%
l 352
14.2%
M 186
 
7.5%
F 166
 
6.7%
m 166
 
6.7%
O 148
 
6.0%
t 148
 
6.0%
h 148
 
6.0%
r 148
 
6.0%

University_Year
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3rd Year
132 
2nd Year
131 
1st Year
125 
4th Year
112 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2nd Year
2nd row1st Year
3rd row4th Year
4th row4th Year
5th row4th Year

Common Values

ValueCountFrequency (%)
3rd Year 132
26.4%
2nd Year 131
26.2%
1st Year 125
25.0%
4th Year 112
22.4%

Length

2024-10-17T19:23:33.123872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T19:23:33.389117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
year 500
50.0%
3rd 132
 
13.2%
2nd 131
 
13.1%
1st 125
 
12.5%
4th 112
 
11.2%

Most occurring characters

ValueCountFrequency (%)
r 632
15.8%
500
12.5%
e 500
12.5%
Y 500
12.5%
a 500
12.5%
d 263
6.6%
t 237
 
5.9%
3 132
 
3.3%
n 131
 
3.3%
2 131
 
3.3%
Other values (4) 474
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 632
15.8%
500
12.5%
e 500
12.5%
Y 500
12.5%
a 500
12.5%
d 263
6.6%
t 237
 
5.9%
3 132
 
3.3%
n 131
 
3.3%
2 131
 
3.3%
Other values (4) 474
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 632
15.8%
500
12.5%
e 500
12.5%
Y 500
12.5%
a 500
12.5%
d 263
6.6%
t 237
 
5.9%
3 132
 
3.3%
n 131
 
3.3%
2 131
 
3.3%
Other values (4) 474
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 632
15.8%
500
12.5%
e 500
12.5%
Y 500
12.5%
a 500
12.5%
d 263
6.6%
t 237
 
5.9%
3 132
 
3.3%
n 131
 
3.3%
2 131
 
3.3%
Other values (4) 474
11.8%

Sleep_Duration
Real number (ℝ)

Distinct51
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4724
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:33.605581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.2
Q15.1
median6.5
Q37.8
95-th percentile8.7
Maximum9
Range5
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.4857635
Coefficient of variation (CV)0.22955372
Kurtosis-1.2791132
Mean6.4724
Median Absolute Deviation (MAD)1.3
Skewness-0.0061837749
Sum3236.2
Variance2.2074932
MonotonicityNot monotonic
2024-10-17T19:23:33.897799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 16
 
3.2%
8.4 16
 
3.2%
4.4 15
 
3.0%
5.2 14
 
2.8%
7.5 14
 
2.8%
4.9 14
 
2.8%
5.1 13
 
2.6%
4.2 13
 
2.6%
7 13
 
2.6%
8 13
 
2.6%
Other values (41) 359
71.8%
ValueCountFrequency (%)
4 2
 
0.4%
4.1 16
3.2%
4.2 13
2.6%
4.3 10
2.0%
4.4 15
3.0%
4.5 9
1.8%
4.6 7
1.4%
4.7 10
2.0%
4.8 11
2.2%
4.9 14
2.8%
ValueCountFrequency (%)
9 5
 
1.0%
8.9 10
2.0%
8.8 8
1.6%
8.7 13
2.6%
8.6 9
1.8%
8.5 7
1.4%
8.4 16
3.2%
8.3 11
2.2%
8.2 7
1.4%
8.1 10
2.0%

Study_Hours
Real number (ℝ)

Distinct116
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9816
Minimum0.1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:34.114219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.695
Q12.9
median6.05
Q38.8
95-th percentile11.6
Maximum12
Range11.9
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation3.4757248
Coefficient of variation (CV)0.58106941
Kurtosis-1.174082
Mean5.9816
Median Absolute Deviation (MAD)2.95
Skewness-0.0047545512
Sum2990.8
Variance12.080663
MonotonicityNot monotonic
2024-10-17T19:23:34.541071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.4 11
 
2.2%
8.3 10
 
2.0%
11.6 9
 
1.8%
7.9 8
 
1.6%
0.5 7
 
1.4%
1.6 7
 
1.4%
5.4 7
 
1.4%
6.2 7
 
1.4%
11.9 7
 
1.4%
8.8 7
 
1.4%
Other values (106) 420
84.0%
ValueCountFrequency (%)
0.1 6
1.2%
0.2 2
 
0.4%
0.3 4
0.8%
0.4 5
1.0%
0.5 7
1.4%
0.6 1
 
0.2%
0.7 7
1.4%
0.8 4
0.8%
0.9 5
1.0%
1 5
1.0%
ValueCountFrequency (%)
12 3
 
0.6%
11.9 7
1.4%
11.8 2
 
0.4%
11.7 5
1.0%
11.6 9
1.8%
11.5 5
1.0%
11.4 2
 
0.4%
11.3 1
 
0.2%
11.2 6
1.2%
11.1 4
0.8%

Screen_Time
Real number (ℝ)

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.525
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:34.742535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q11.8
median2.6
Q33.3
95-th percentile3.8
Maximum4
Range3
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.85941386
Coefficient of variation (CV)0.34036193
Kurtosis-1.184962
Mean2.525
Median Absolute Deviation (MAD)0.8
Skewness-0.071633318
Sum1262.5
Variance0.73859218
MonotonicityNot monotonic
2024-10-17T19:23:34.933031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.6 22
 
4.4%
2.8 21
 
4.2%
3.4 21
 
4.2%
3.5 21
 
4.2%
1.8 20
 
4.0%
3.2 20
 
4.0%
3 19
 
3.8%
2.4 19
 
3.8%
2.3 19
 
3.8%
1.2 19
 
3.8%
Other values (21) 299
59.8%
ValueCountFrequency (%)
1 8
 
1.6%
1.1 14
2.8%
1.2 19
3.8%
1.3 18
3.6%
1.4 14
2.8%
1.5 13
2.6%
1.6 18
3.6%
1.7 14
2.8%
1.8 20
4.0%
1.9 16
3.2%
ValueCountFrequency (%)
4 9
1.8%
3.9 14
2.8%
3.8 11
2.2%
3.7 17
3.4%
3.6 22
4.4%
3.5 21
4.2%
3.4 21
4.2%
3.3 14
2.8%
3.2 20
4.0%
3.1 13
2.6%

Caffeine_Intake
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.462
Minimum0
Maximum5
Zeros85
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:35.154393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6823247
Coefficient of variation (CV)0.68331629
Kurtosis-1.2310121
Mean2.462
Median Absolute Deviation (MAD)1
Skewness0.0077235791
Sum1231
Variance2.8302164
MonotonicityNot monotonic
2024-10-17T19:23:35.437636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 92
18.4%
4 88
17.6%
0 85
17.0%
3 82
16.4%
1 79
15.8%
5 74
14.8%
ValueCountFrequency (%)
0 85
17.0%
1 79
15.8%
2 92
18.4%
3 82
16.4%
4 88
17.6%
5 74
14.8%
ValueCountFrequency (%)
5 74
14.8%
4 88
17.6%
3 82
16.4%
2 92
18.4%
1 79
15.8%
0 85
17.0%

Physical_Activity
Real number (ℝ)

Distinct120
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.342
Minimum0
Maximum120
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:35.677991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q132.75
median62.5
Q393.25
95-th percentile115
Maximum120
Range120
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation35.191674
Coefficient of variation (CV)0.56449383
Kurtosis-1.2306294
Mean62.342
Median Absolute Deviation (MAD)30.5
Skewness-0.088016992
Sum31171
Variance1238.4539
MonotonicityNot monotonic
2024-10-17T19:23:35.916395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 9
 
1.8%
27 9
 
1.8%
99 8
 
1.6%
108 8
 
1.6%
83 8
 
1.6%
92 8
 
1.6%
86 7
 
1.4%
85 7
 
1.4%
52 7
 
1.4%
46 7
 
1.4%
Other values (110) 422
84.4%
ValueCountFrequency (%)
0 3
0.6%
1 1
 
0.2%
2 4
0.8%
3 5
1.0%
4 4
0.8%
5 4
0.8%
6 5
1.0%
7 6
1.2%
8 4
0.8%
9 6
1.2%
ValueCountFrequency (%)
120 4
0.8%
119 5
1.0%
118 5
1.0%
117 3
0.6%
116 4
0.8%
115 6
1.2%
114 4
0.8%
113 5
1.0%
112 3
0.6%
111 3
0.6%

Sleep_Quality
Real number (ℝ)

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.362
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:36.105889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9672487
Coefficient of variation (CV)0.5533847
Kurtosis-1.2742452
Mean5.362
Median Absolute Deviation (MAD)3
Skewness0.040661416
Sum2681
Variance8.8045651
MonotonicityNot monotonic
2024-10-17T19:23:36.295341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 66
13.2%
6 57
11.4%
9 55
11.0%
3 54
10.8%
10 50
10.0%
2 46
9.2%
4 46
9.2%
7 45
9.0%
5 41
8.2%
8 40
8.0%
ValueCountFrequency (%)
1 66
13.2%
2 46
9.2%
3 54
10.8%
4 46
9.2%
5 41
8.2%
6 57
11.4%
7 45
9.0%
8 40
8.0%
9 55
11.0%
10 50
10.0%
ValueCountFrequency (%)
10 50
10.0%
9 55
11.0%
8 40
8.0%
7 45
9.0%
6 57
11.4%
5 41
8.2%
4 46
9.2%
3 54
10.8%
2 46
9.2%
1 66
13.2%

Weekday_Sleep_Start
Real number (ℝ)

Distinct452
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.16686
Minimum1.08
Maximum21.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:36.488821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.08
5-th percentile2.229
Q16.0875
median10.635
Q316.1525
95-th percentile20.841
Maximum21.93
Range20.85
Interquartile range (IQR)10.065

Descriptive statistics

Standard deviation5.972352
Coefficient of variation (CV)0.53482823
Kurtosis-1.1495632
Mean11.16686
Median Absolute Deviation (MAD)5
Skewness0.10794781
Sum5583.43
Variance35.668988
MonotonicityNot monotonic
2024-10-17T19:23:36.774093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.29 3
 
0.6%
13.29 2
 
0.4%
6.54 2
 
0.4%
12.49 2
 
0.4%
5.69 2
 
0.4%
2.08 2
 
0.4%
5.42 2
 
0.4%
13.26 2
 
0.4%
7.31 2
 
0.4%
3.76 2
 
0.4%
Other values (442) 479
95.8%
ValueCountFrequency (%)
1.08 1
0.2%
1.17 2
0.4%
1.22 1
0.2%
1.24 1
0.2%
1.28 1
0.2%
1.31 1
0.2%
1.32 1
0.2%
1.33 1
0.2%
1.35 1
0.2%
1.5 1
0.2%
ValueCountFrequency (%)
21.93 1
0.2%
21.89 2
0.4%
21.88 1
0.2%
21.86 1
0.2%
21.82 1
0.2%
21.71 1
0.2%
21.69 1
0.2%
21.68 1
0.2%
21.67 2
0.4%
21.64 1
0.2%

Weekend_Sleep_Start
Real number (ℝ)

Distinct442
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.37586
Minimum2.05
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:36.990479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.05
5-th percentile3.1685
Q17.2975
median12.69
Q317.3275
95-th percentile20.9905
Maximum22
Range19.95
Interquartile range (IQR)10.03

Descriptive statistics

Standard deviation5.7896108
Coefficient of variation (CV)0.46781483
Kurtosis-1.2171067
Mean12.37586
Median Absolute Deviation (MAD)4.975
Skewness-0.11019528
Sum6187.93
Variance33.519593
MonotonicityNot monotonic
2024-10-17T19:23:37.220904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.53 3
 
0.6%
8.96 3
 
0.6%
20.47 3
 
0.6%
15.25 3
 
0.6%
15.86 2
 
0.4%
15.36 2
 
0.4%
2.46 2
 
0.4%
8.67 2
 
0.4%
4.11 2
 
0.4%
15.63 2
 
0.4%
Other values (432) 476
95.2%
ValueCountFrequency (%)
2.05 1
0.2%
2.06 1
0.2%
2.07 1
0.2%
2.11 2
0.4%
2.17 1
0.2%
2.2 1
0.2%
2.22 1
0.2%
2.25 1
0.2%
2.28 1
0.2%
2.3 1
0.2%
ValueCountFrequency (%)
22 1
0.2%
21.97 1
0.2%
21.89 1
0.2%
21.87 1
0.2%
21.84 1
0.2%
21.81 1
0.2%
21.73 1
0.2%
21.66 1
0.2%
21.63 1
0.2%
21.53 1
0.2%

Weekday_Sleep_End
Real number (ℝ)

Distinct289
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9299
Minimum5
Maximum8.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:37.450629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.149
Q15.9
median6.885
Q37.9725
95-th percentile8.74
Maximum8.98
Range3.98
Interquartile range (IQR)2.0725

Descriptive statistics

Standard deviation1.1831742
Coefficient of variation (CV)0.17073467
Kurtosis-1.2740428
Mean6.9299
Median Absolute Deviation (MAD)1.05
Skewness0.036455298
Sum3464.95
Variance1.3999012
MonotonicityNot monotonic
2024-10-17T19:23:37.688991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.98 5
 
1.0%
8.45 5
 
1.0%
5.27 4
 
0.8%
5.1 4
 
0.8%
6.23 4
 
0.8%
5.78 4
 
0.8%
5.24 4
 
0.8%
7.92 4
 
0.8%
5.22 4
 
0.8%
5.93 4
 
0.8%
Other values (279) 458
91.6%
ValueCountFrequency (%)
5 1
 
0.2%
5.01 1
 
0.2%
5.02 1
 
0.2%
5.03 2
0.4%
5.04 2
0.4%
5.05 2
0.4%
5.06 3
0.6%
5.07 2
0.4%
5.08 3
0.6%
5.09 1
 
0.2%
ValueCountFrequency (%)
8.98 5
1.0%
8.97 1
 
0.2%
8.95 2
 
0.4%
8.94 1
 
0.2%
8.92 1
 
0.2%
8.91 2
 
0.4%
8.87 1
 
0.2%
8.86 1
 
0.2%
8.85 2
 
0.4%
8.84 1
 
0.2%

Weekend_Sleep_End
Real number (ℝ)

Distinct297
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9881
Minimum7.02
Maximum10.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-10-17T19:23:37.927353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.02
5-th percentile7.2295
Q18.0475
median9.005
Q39.925
95-th percentile10.75
Maximum10.99
Range3.97
Interquartile range (IQR)1.8775

Descriptive statistics

Standard deviation1.1112528
Coefficient of variation (CV)0.12363601
Kurtosis-1.1323291
Mean8.9881
Median Absolute Deviation (MAD)0.945
Skewness0.00021165508
Sum4494.05
Variance1.2348828
MonotonicityNot monotonic
2024-10-17T19:23:38.177683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.81 5
 
1.0%
10.17 5
 
1.0%
8.61 5
 
1.0%
10.41 4
 
0.8%
8.83 4
 
0.8%
9.07 4
 
0.8%
8.78 4
 
0.8%
10.25 4
 
0.8%
9.48 4
 
0.8%
8.95 4
 
0.8%
Other values (287) 457
91.4%
ValueCountFrequency (%)
7.02 1
 
0.2%
7.03 1
 
0.2%
7.04 3
0.6%
7.06 1
 
0.2%
7.07 1
 
0.2%
7.08 2
0.4%
7.09 1
 
0.2%
7.1 1
 
0.2%
7.11 1
 
0.2%
7.12 1
 
0.2%
ValueCountFrequency (%)
10.99 2
0.4%
10.98 1
0.2%
10.97 1
0.2%
10.95 1
0.2%
10.94 2
0.4%
10.92 2
0.4%
10.91 1
0.2%
10.89 1
0.2%
10.88 1
0.2%
10.87 1
0.2%

Interactions

2024-10-17T19:23:29.106490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:07.717866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:09.513113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:11.543210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:13.448111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:15.374998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:17.346446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:19.187518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:21.287900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:23.087082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:24.945108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:27.054541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:29.283055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:07.865471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:09.684656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:11.682880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:13.595758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:15.539552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:17.497042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:19.349088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:21.422575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:23.238714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:25.102959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:27.210606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:29.488470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:08.017103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:09.859189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:11.847396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:13.749344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:15.718074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:17.646684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:19.518632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:21.574131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:23.401242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:25.272467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:27.391121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:29.694915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:08.158731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:10.017765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:11.997038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:13.896913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:15.883593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:17.790295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:19.802873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:21.717746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:23.551839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:25.424103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:27.560630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:29.857518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:08.311330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:10.189303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:12.146635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:14.047509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:16.051182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:17.938859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:19.971420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:21.866349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:23.703435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:25.589618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:27.734163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:30.018089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:08.458935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:10.379795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:12.294199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:14.198107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:16.211718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:18.094444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:20.144958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:22.010961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:23.868990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:25.755175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:27.898723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:30.172674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:08.604546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:10.611175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:12.436821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:14.350734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:16.362313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:18.233074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:20.307521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:22.149590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:24.015597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:25.909605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:28.061287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:30.330253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:08.754145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:10.773271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:12.591442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:14.607011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:16.518893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:18.377686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:20.463105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:22.303182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:24.157255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:26.077157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:28.225885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:30.481811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:08.887787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:10.910902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:12.738015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:14.740652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:16.659516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:18.513323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:20.637637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:22.431835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:24.287908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:26.387327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:28.381431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:30.627459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:09.015445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:11.048534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:12.877639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:14.881278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:16.794156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:18.643013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:20.780255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:22.556501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:24.413532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:26.529947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:28.538013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:30.805943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:09.179009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:11.214137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:13.061149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:15.045837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:16.965697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:18.825487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:20.946810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:22.714080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:24.583078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:26.702486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:28.725548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:30.982472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:09.340575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:11.378697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:13.233685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:15.209398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:17.131254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:19.008996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:21.122381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:22.869663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:24.778555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:26.881044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T19:23:28.917994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-17T19:23:38.362189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeCaffeine_IntakeGenderPhysical_ActivityScreen_TimeSleep_DurationSleep_QualityStudent_IDStudy_HoursUniversity_YearWeekday_Sleep_EndWeekday_Sleep_StartWeekend_Sleep_EndWeekend_Sleep_Start
Age1.0000.0080.0000.014-0.085-0.0150.020-0.0110.0600.0000.0210.0110.0580.010
Caffeine_Intake0.0081.0000.060-0.0270.050-0.013-0.006-0.0260.0330.0300.007-0.0040.0080.022
Gender0.0000.0601.0000.0610.0000.0000.0000.0640.0000.0000.0000.0000.0770.000
Physical_Activity0.014-0.0270.0611.000-0.037-0.005-0.0140.058-0.0470.0640.034-0.0040.0230.040
Screen_Time-0.0850.0500.000-0.0371.0000.0710.010-0.032-0.0410.000-0.017-0.070-0.100-0.046
Sleep_Duration-0.015-0.0130.000-0.0050.0711.000-0.0150.048-0.0100.070-0.035-0.077-0.0460.035
Sleep_Quality0.020-0.0060.000-0.0140.010-0.0151.0000.0130.0580.0490.023-0.014-0.006-0.003
Student_ID-0.011-0.0260.0640.058-0.0320.0480.0131.000-0.0550.0000.0500.038-0.067-0.043
Study_Hours0.0600.0330.000-0.047-0.041-0.0100.058-0.0551.0000.0400.011-0.0060.056-0.009
University_Year0.0000.0300.0000.0640.0000.0700.0490.0000.0401.0000.0390.0300.0280.000
Weekday_Sleep_End0.0210.0070.0000.034-0.017-0.0350.0230.0500.0110.0391.000-0.032-0.000-0.051
Weekday_Sleep_Start0.011-0.0040.000-0.004-0.070-0.077-0.0140.038-0.0060.030-0.0321.0000.1150.024
Weekend_Sleep_End0.0580.0080.0770.023-0.100-0.046-0.006-0.0670.0560.028-0.0000.1151.0000.075
Weekend_Sleep_Start0.0100.0220.0000.040-0.0460.035-0.003-0.043-0.0090.000-0.0510.0240.0751.000

Missing values

2024-10-17T19:23:31.227854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-17T19:23:31.581865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Student_IDAgeGenderUniversity_YearSleep_DurationStudy_HoursScreen_TimeCaffeine_IntakePhysical_ActivitySleep_QualityWeekday_Sleep_StartWeekend_Sleep_StartWeekday_Sleep_EndWeekend_Sleep_End
0124Other2nd Year7.77.93.42371014.164.057.417.06
1221Male1st Year6.36.01.957428.737.108.2110.21
2322Male4th Year5.16.73.9553520.0020.476.8810.92
3424Other4th Year6.38.62.8455919.824.086.699.42
4520Male4th Year4.72.72.7085320.986.128.989.01
5625Other1st Year4.912.03.239699.8018.835.0410.51
6722Female2nd Year6.511.73.4199613.0520.968.5810.81
7822Male2nd Year6.17.83.01108410.4910.855.6010.02
8924Female1st Year8.62.41.4186711.0618.888.148.78
91019Other2nd Year5.88.22.0344814.655.317.479.37
Student_IDAgeGenderUniversity_YearSleep_DurationStudy_HoursScreen_TimeCaffeine_IntakePhysical_ActivitySleep_QualityWeekday_Sleep_StartWeekend_Sleep_StartWeekday_Sleep_EndWeekend_Sleep_End
49049125Other3rd Year4.47.52.3391316.966.175.698.00
49149222Female4th Year4.34.62.2075318.973.686.919.04
49249318Other1st Year5.22.53.7437619.5714.807.9510.64
49349423Female2nd Year4.81.53.11114821.4315.865.5610.14
49449522Other3rd Year8.47.42.8435711.0018.106.768.95
49549624Male2nd Year5.19.31.94110417.428.436.9310.78
49649720Male2nd Year8.97.73.534041.2215.545.857.23
49749821Male3rd Year5.76.43.9168109.942.255.4610.72
49849918Female2nd Year4.90.53.5012219.1015.498.357.20
49950021Male3rd Year7.911.61.008617.5414.127.019.19